Human teamwork is often threatened by the inclination of information overload [18] and the complexity of distributed cognition [2]. However, most applications for dynamic domains require members of a human team to cooperate effectively in information gathering, information fusion, sense-making, information delivering, and group decision makings. It is thus highly critical to investigate novel ideas and develop effective solutions to reduce the threats from information overload and distributed cognition to an acceptable level.
Take the domain of homeland security (HS) as an example. The cognitive demands in HS are complex, dynamic, and time pressured:                1. An HS team has to process voluminous amount of information that is dynamic, changing, and uncertain in nature;        2. The data, information, and knowledge resident within the broad scope of homeland security situations are distributed across people, objects, tools, and environments. For example, one member of a HS team may be an expert in terrorist organizations, while another is an expert in biological threats, and they may process information using completely different databases and tools;        3. Team members can have differing levels of access to various information sources, due to security concerns often associated with their roles and responsibilities. For example, an analyst may have access to satellite images while another analyst may have access to intelligence reports;        4. To enable early detection and successful processing of potential terrorist threats, team members must effectively work together to quickly make sense of the information from multiple sources.        
These unique and complex challenges can significantly hamper the quality and the timeliness of decision-making in homeland security areas, which can have extraordinary and possibly catastrophic consequences.
The approach adopted in this invention is to develop a cognitive-aware software system that can act as decision aids of human team members in varying ways, including context-sensitive anticipation of others' information needs, proactive information/experience sharing, and collaborative situation assessment.
There has been much theory and research presented with respect to team cognition, naturalistic decision-making, and collaborative technology [8] in relation to real world, complex domains of practice. However, there has been very little work in looking at cognitive agent architectures as a means to assist and support distributed team cognition and decision-making. This is particularly true as applied to naturalistic decision-making theory (e.g., Klein's RPD framework [4]).
As one examines naturalistic decision-making in complex domains, there are several factors that are salient to consider. One theory that attempts to understand the dynamics of team cognition introduces the concept of a “shared mental model” [1][16] which refers to an overlapping understanding among members of the team regarding their objectives, structure, process, etc. Effective teams exhibit shared mental models wherein members can anticipate the needs of and proactively offer help to other teammates [3]. Along this direction, Yen et al. implemented a team-oriented agent architecture called CAST [19], which realized a computational shared mental model and allows agents in a team, whether they are software agents or human agents, to anticipate the potential information needs of teammates and proactively assist them.
Background on Recognition-Primed Decisions (RPD)
Assuming that satisfying reflects human behavior better than optimizing [13], the RPD model argues for finding the first workable solution through recognizing the similarity between the current decision situation and previous decision experiences. The RPD model divides a decision-making process into two phases [5]: recognizing which course of action makes sense for the current situation; then evaluating the course of action by imagining it.
Feature-matching and story-building are two diagnostic strategies employed in the first phase to develop situation awareness. Feature-matching is used first, by which a decision maker tries to match the set of observed cues or pattern of cues with the pre-determined domain-dependent features. In case feature-matching cannot provide an adequate situation resolution due to lack of information or experience, story-building is adopted to construct a story (i.e., a causal sequence of events) that can link the pieces of observed and available information into a coherent form. The story provides an explanation of how the current situation might have been emerging.
To explain the observed events, both strategies require that a decision maker determine expectancies (prescribe what to be observed next as the situation continues to evolve) and relevant cues (what the decision maker needs to pay attention to) pertinent to the current situation, and make assumptions for the missing information. A decision maker may have misinterpreted the situation but does not realize it until some expectancies have been violated. Thus, expectancies serve as gate-conditions for continuing working on the current recognition; further diagnosis is triggered (to gather more information) whenever the expectancies conflict with new observed facts. Assumptions serve as slots for gaining confidence; the recognition is more justified as more available information validates the assumptions. To better explain the observed events, story-building also allows a decision maker to explore several potential hypotheses and evaluate how well each of them fits the observations.
In addition to expectancies and relevant cues, the recognition phase can also result in a set of plausible goals (which goals make sense) and courses of action (what actions worked before in similar situations). The main task in the second phase is to carry out singular evaluation by imaging how the course of action will evolve. The decision maker may need to adjust the course of action, or reject it and look for another option.
There have been several attempts in computerizing the RPD model. For example, there are RPD-related studies that used long-term memory structure [17], fuzzy techniques [12], and neural networks [7] to represent experiences. The Navy DSS system [9] implements a functional model that allows a decision maker to explore alternative hypothesis. There are also attempts in integrating RPD with agent technologies [10][14][17][20]. For example, Norling et al. [10] explored the ways of using RPD to enhance BDI agents so that the simulations of human societies would be more realistic. Warwick, et al. [17][20] investigated a computational approach to RPD to represent human decision-making for concept exploration, analysis, or evaluation. However, all of these attempts have ignored the support of team collaboration in the RPD decision-making process. This actually leaves the most exciting part of the RPD model as an open research issue: how a team of agents, with a shared computational RPD process, are supposed to work together in collaboratively developing situation awareness, in effectively anticipating others' information needs relevant to cues and expectancies, and in proactively sharing information to make better decisions under time pressure.
Others have been investigating (a) the use of software agents for robust battlefield simulation (e.g., [21]); (b) the use of agents as aids to information filtering in a decision environment (e.g., [22][23]); (c) shared situation awareness (e.g., [24]), cognitive models of situation awareness (e.g., [25]); and (d) teaming with automation (e.g., [26]). The collaborative-RPD model implemented in R-CAST is linked to but also distinguished from the existing work in important ways. First, R-CAST is the first RPD-enabled agent architecture designed for supporting teamwide collaborations (including human-agent and agent-agent collaborations). With collaboration in mind, we take an intensive view of the recognition phase of the RPD process and focus on the investigation of how proactive information exchange among teammates might affect the performance of a decision-making team. Second, R-CAST agents can proactively reason across decision spaces, seek missing information from external intelligence sources, exchange relevant information among teammates, and monitor an on-going decision against potential expectancy. Third, the “cognitively-aware” agents, as teammates or decision aids, each assigned to a specific functional area, can be used to assist human teams (e.g., military staff) in developing shared situation awareness while balancing information requirements against the dynamic and time sensitive decision-making process.
Case-based reasoning (CBR) is another psychological theory of human cognition [27], focusing on the process of reminding (experience-guided reasoning) and learning. While there is no clear line between RPD and CBR as far as their process models are concerned (e.g., both cover experience retrieval, solution adaptation and evaluation), they differ in several important aspects. First, RPD originates from studies about how human experts make decisions under time pressure [5][6]. Experiences in RPD are prior decision-making cases, while experiences in CBR can be of any kind. From such a perspective, RPD can be taken as a subfield of CBR. Second, while storage and retrieval are central aspects of CBR, research on RPD is more concerned with the iterative process of recognition refinement (i.e., developing better situation awareness through information gathering). Third, RPD systems ought to be aware of time stress and make as better decisions as time permitted, but this is not a requirement on CBR systems. In addition, the Collaborative-RPD model implemented in R-CAST takes a more extensive view, focusing not only on human-centered teamwork in making decisions, but also seriously addressing related issues such as collaborative situation awareness and expectancy monitoring.
Background on CAST
CAST (Collaborative Agents for Simulating Teamwork) is a team-oriented agent architecture that supports teamwork using a shared mental model among teammates [19]. The structure of the team (roles, agents, subteams, etc.) as well as team processes (plans to achieve various team tasks) are explicitly described in a declarative language called MALLET that was designed for this purpose. Statements in MALLET are translated into PrT net (a specialized Petri-Net), which uses predicate evaluation at decision points.
CAST supports predicate evaluation using a knowledge base with a Java-based backward chaining reasoning engine called JARE. The main distinguishing feature of CAST is proactive team behavior enabled by the fact that agents within a CAST architecture share the same declarative specification of team structure and process as well as sharing an explicit declaration of what each agent can observe. Therefore, every agent can reason about what other teammates are working on, what information they need, whether they can observe the information required to evaluate a precondition, and hence what information might be potentially useful to them. As such, agents can figure out what information to proactively deliver to teammates, and use a decision theoretic cost/benefit analysis of the proactive information delivery before actually communicating.
As shown in FIG. 1, a CAST agent is composed of six components: Reasoning Engine (RE), Shared Mental Model (SMM), Individual Mental Model (IMM), Team Process Tracking (TPT), Proactive Behavior (PB), and Goal Management (GM). Based on the current states of SMM and IMM, the RE triggers appropriate algorithms in TPT, PB and GM to monitor the progress of team activities, to select goals to pursue, to anticipate others' information needs and to proactively help them. The execution of these mental operations will further affect the evolution of the shared and individual mental states.
The proactive information delivery behavior of CAST agents is based on the reasoning of others' information needs. CAST supports three kinds of information-needs. (1) Built-in information needs: each agent needs to know others' progress in order to maintain the SMM regarding the dynamic status of a team process. Such built-in information-needs provide the cohesive force that binds individual CAST agents together as a team. (2) Inferred information needs: CAST agents can extract the pre-conditions, termination conditions and constraints associated with (sub-)plans in a team process, and establish partial information-flow relationships based on incomplete knowledge. These partial relationships can be further refined as the team allocates tasks at run time. (3) Communicated information needs: an agent treats requests of information from others as explicit needs.